Benchmarking Neural Networks Activation Functions for Cancer Detection

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The choice of the most suitable activation functions for artificial neural networks significantly affects training time and task performance. Breast cancer detection is currently based on the use of neural networks and their selection is an element that affects performance. In the present work, reference information on activation functions in neural networks was analyzed. Exploratory research, comprehensive reading, stepwise approach, and deduction were applied as a method. It resulted in phases of comparative evaluation inactivation functions, a quantitative and qualitative comparison of activation functions, and a prototype of neural network algorithm with activation function to detect cancer; It was concluded that the final results put as the best option to use ReLU for early detection of cancer.

Original languageEnglish
Title of host publicationHuman Interaction, Emerging Technologies and Future Systems V - Proceedings of the 5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and the 6th IHIET
Subtitle of host publicationFuture Systems IHIET-FS 2021
EditorsTareq Ahram, Redha Taiar
PublisherSpringer Science and Business Media Deutschland GmbH
Pages867-873
Number of pages7
ISBN (Print)9783030855390
DOIs
StatePublished - 2022
Event5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021 - Virtual, Online
Duration: 27 Aug 202129 Aug 2021

Publication series

NameLecture Notes in Networks and Systems
Volume319
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference5th International Virtual Conference on Human Interaction and Emerging Technologies, IHIET 2021 and 6th International Conference on Human Interaction and Emerging Technologies: Future Systems, IHIET-FS 2021
CityVirtual, Online
Period27/08/2129/08/21

Bibliographical note

Funding Information:
This work has been supported by the GIIAR research group and the Universidad Polit?cnica Salesiana.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Activation functions
  • Benchmarking
  • Cancer detection
  • Neural networks

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